Dysregulation of the Suprachiasmatic Nucleus Disturbs the Circadian Rhythm and Aggravates Epileptic Seizures by Inducing Hippocampal GABAergic Dysfunction in C57BL/6 Mice.

The interplay between circadian rhythms and epilepsy has gained increasing attention. The suprachiasmatic nucleus (SCN), which acts as the master circadian pacemaker, regulates physiological and behavioral rhythms through its complex neural networks. However, the exact role of the SCN and its Bmal1 gene in the development of epilepsy remains unclear. In this study, we utilized a lithium-pilocarpine model to induce epilepsy in mice and simulated circadian disturbances by creating lesions in the SCN and specifically knocking out the Bmal1 gene in the SCN neurons. We observed that the pilocarpine-induced epileptic mice experienced increased daytime seizure frequency, irregular oscillations in core body temperature, and circadian gene alterations in both the SCN and the hippocampus. Additionally, there was enhanced activation of GABAergic projections from the SCN to the hippocampus. Notably, SCN lesions intensified seizure activity, concomitant with hippocampal neuronal damage and GABAergic signaling impairment. Further analyses using the Gene Expression Omnibus database and gene set enrichment analysis indicated reduced Bmal1 expression in patients with medial temporal lobe epilepsy, potentially affecting GABA receptor pathways. Targeted deletion of Bmal1 in SCN neurons exacerbated seizures and pathology in epilepsy, as well as diminished hippocampal GABAergic efficacy. These results underscore the crucial role of the SCN in modulating circadian rhythms and GABAergic function in the hippocampus, aggravating the severity of seizures. This study provides significant insights into how circadian rhythm disturbances can influence neuronal dysfunction and epilepsy, highlighting the therapeutic potential of targeting SCN and the Bmal1 gene within it in epilepsy management.

IRAK-M Ablation Promotes Status Epilepticus-Induced Neuroinflammation via Activating M1 Microglia and Impairing Excitatory Synaptic Function.

Epilepsy is one of the most common neurological disorders. The pro-epileptic and antiepileptic roles of microglia have recently garnered significant attention. Interleukin-1 receptor-associated kinase (IRAK)-M, an important kinase in the innate immune response, is mainly expressed in microglia and acts as a negative regulator of the TLR4 signaling pathway that mediates the anti-inflammatory effect. However, whether IRAK-M exerts a protective role in epileptogenesis as well as the molecular and cellular mechanisms underlying these processes are yet to be elucidated. An epilepsy mouse model induced by pilocarpine was used in this study. Real-time quantitative polymerase chain reaction and western blot analysis were used to analyze mRNA and protein expression levels, respectively. Whole-cell voltage-clamp recordings were employed to evaluate the glutamatergic synaptic transmission in hippocampal neurons. Immunofluorescence was utilized to show the glial cell activation and neuronal loss. Furthermore, the proportion of microglia was analyzed using flow cytometry. Seizure dynamics influenced the expression of IRAK-M. Its knockout dramatically exacerbated the seizures and the pathology in epilepsy and increased the N-methyl-d-aspartate receptor (NMDAR) expression, thereby enhancing glutamatergic synaptic transmission in hippocampal CA1 pyramidal neurons in mice. Furthermore, IRAK-M deficiency augmented hippocampal neuronal loss via a possible mechanism of NMDAR-mediated excitotoxicity. IRAK-M deletion promotes microglia toward the M1 phenotype, which resulted in high levels of proinflammatory cytokines and was accompanied by a visible increase in the expressions of key microglial polarization-related proteins, including p-STAT1, TRAF6, and SOCS1. The findings demonstrate that IRAK-M dysfunction contributes to the progression of epilepsy by increasing M1 microglial polarization and glutamatergic synaptic transmission. This is possibly related to NMDARs, particularly Grin2A and Grin2B, which suggests that IRAK-M could serve as a novel therapeutic target for the direct alleviation of epilepsy.

Sparse logistic regression revealed the associations between HBV PreS quasispecies and hepatocellular carcinoma.

BACKGROUND: Chronic infection with hepatitis B virus (HBV) has been proved highly associated with the development of hepatocellular carcinoma (HCC). AIMS: The purpose of the study is to investigate the association between HBV preS region quasispecies and HCC development, as well as to develop HCC diagnosis model using HBV preS region quasispecies. METHODS: A total of 104 chronic hepatitis B (CHB) patients and 117 HBV-related HCC patients were enrolled. HBV preS region was sequenced using next generation sequencing (NGS) and the nucleotide entropy was calculated for quasispecies evaluation. Sparse logistic regression (SLR) was used to predict HCC development and prediction performances were evaluated using receiver operating characteristic curves. RESULTS: Entropy of HBV preS1, preS2 regions and several nucleotide points showed significant divergence between CHB and HCC patients. Using SLR, the classification of HCC/CHB groups achieved a mean area under the receiver operating characteristic curve (AUC) of 0.883 in the training data and 0.795 in the test data. The prediction model was also validated by a completely independent dataset from Hong Kong. The 10 selected nucleotide positions showed significantly different entropy between CHB and HCC patients. The HBV quasispecies also classified three clinical parameters, including HBeAg, HBVDNA, and Alkaline phosphatase (ALP) with the AUC value greater than 0.6 in the test data. CONCLUSIONS: Using NGS and SLR, the association between HBV preS region nucleotide entropy and HCC development was validated in our study and this could promote the understanding of HCC progression mechanism.

Deep sequencing of HBV pre-S region reveals high heterogeneity of HBV genotypes and associations of word pattern frequencies with HCC.

Hepatitis B virus (HBV) infection is a common problem in the world, especially in China. More than 60-80% of hepatocellular carcinoma (HCC) cases can be attributed to HBV infection in high HBV prevalent regions. Although traditional Sanger sequencing has been extensively used to investigate HBV sequences, NGS is becoming more commonly used. Further, it is unknown whether word pattern frequencies of HBV reads by Next Generation Sequencing (NGS) can be used to investigate HBV genotypes and predict HCC status. In this study, we used NGS to sequence the pre-S region of the HBV sequence of 94 HCC patients and 45 chronic HBV (CHB) infected individuals. Word pattern frequencies among the sequence data of all individuals were calculated and compared using the Manhattan distance. The individuals were grouped using principal coordinate analysis (PCoA) and hierarchical clustering. Word pattern frequencies were also used to build prediction models for HCC status using both K-nearest neighbors (KNN) and support vector machine (SVM). We showed the extremely high power of analyzing HBV sequences using word patterns. Our key findings include that the first principal coordinate of the PCoA analysis was highly associated with the fraction of genotype B (or C) sequences and the second principal coordinate was significantly associated with the probability of having HCC. Hierarchical clustering first groups the individuals according to their major genotypes followed by their HCC status. Using cross-validation, high area under the receiver operational characteristic curve (AUC) of around 0.88 for KNN and 0.92 for SVM were obtained. In the independent data set of 46 HCC patients and 31 CHB individuals, a good AUC score of 0.77 was obtained using SVM. It was further shown that 3000 reads for each individual can yield stable prediction results for SVM. Thus, another key finding is that word patterns can be used to predict HCC status with high accuracy. Therefore, our study shows clearly that word pattern frequencies of HBV sequences contain much information about the composition of different HBV genotypes and the HCC status of an individual.

Next-generation sequencing revealed divergence in deletions of the preS region in the HBV genome between different HBV-related liver diseases.

In order to investigate if deletion patterns of the preS region can predict liver disease advancement, the preS region of the hepatitis B virus (HBV) genome in 45 chronic hepatitis B (CHB) and 94 HBV-related hepatocellular carcinoma (HCC) patients was sequenced by next-generation sequencing (NGS) and the percentages of nucleotide deletion in the preS region were analysed. Hierarchical clustering and heatmaps based on deletion percentages of preS revealed different deletion patterns between CHB and HCC patients. Intergenotype comparison also indicated divergence in preS deletions between HBV genotype B and C. No significant difference was found in preS deletion patterns between sera and matched adjacent non-tumour tissues. Based on hierarchical clustering, HCC patients were classed into two groups with different preS deletion patterns and different clinical features. Finally, the support vector machine (SVM) model was trained on preS nucleotide deletion percentages and used to predict HCC versus CHB patients. The prediction performance was assessed with fivefold cross-validation and independent cohort validation. The median area under the curve (AUC) was 0.729 after repeating SVM 500 times with fivefold cross-validations. After parameter optimization, the SVM model was used to predict an independent cohort with 51 CHB patients and 72 HCC patients and the AUC was 0.727. In conclusion, the use of the NGS method revealed a prominent divergence in preS deletion patterns between disease groups and virus genotypes, but not between different tissue types. Quantitative NGS data combined with a machine learning method could be a powerful approach for prediction of the status of different diseases.